# Phase 5 — Model Failure Triage Findings

Source: 112 completed cells across 3 Together models (Qwen/Qwen3.5-9B,
Qwen/Qwen3.6-Plus, google/gemma-4-31B-it), 12 engine-feature scenario
packs × 2 levels (easy, medium) × 2 seeds × vision fog mode. Per-cell
JSONL captures the full untruncated turn-by-turn record (obs,
system_prompt, briefing, model_request, model_response, commands,
signals, terminal). Triage generated by `scripts/triage_phase4.py`.

> **⚠ CORRECTION (2026-05-23): the original F1 headline — "Plus is
> passive" — is RETRACTED. Root cause is a Together-API adapter bug
> dropping Plus's tool_calls from the wire response. Plus IS reasoning
> and emitting tool calls server-side; the bench parser receives an
> empty `tool_calls` list and falls back to the default Observe. See
> §F1-RETRACTED below for the full diagnosis. Phase-5 headline is now
> the F3 perception-axis result (target visibility predicts win rate),
> documented below.**

## Outcome matrix

### Qwen/Qwen3.5-9B (48 cells)
| pack                          | easy   | medium |
|-------------------------------|--------|--------|
| combat-naval-shore-strike     | 2W     | 1W 1L  |
| def-bridge-chokepoint         | 1W 1L  | 2W     |
| econ-contested-expansion      | 2L     | 2L     |
| econ-harvester-defense-raid   | 2W     | 2L     |
| econ-mine-and-grow            | 2L     | 2L     |
| econ-multi-patch-allocation   | 2L     | 2L     |
| econ-second-base-race         | 2W     | 2L     |
| spec-engineer-capture         | 2W     | 2L     |
| spec-nuke-strike              | 2L     | 2L     |
| spec-spy-infiltrate           | 2W     | 2L     |
| spec-tanya-c4-strike          | 2W     | 2W     ← perfect 4/4
| spec-thief-steal-cash         | 2W     | 1L 1D  |
**Totals: 22W, 23L, 1D = 48% win on easy, 17% win on medium**

### google/gemma-4-31B-it (9 cells, partial)
| pack                          | easy   | medium |
|-------------------------------|--------|--------|
| spec-tanya-c4-strike          | 2W     | 1W 1L  |
| spec-engineer-capture         | 2W     | -      |
| (others in flight)            |        |        |
**Partial: 5W / 4L / 0D = 55.6% win**

### Qwen/Qwen3.6-Plus (55 cells, **EXCLUDED from headline**)
All cells issued `Observe` only (default fallback) due to the adapter
bug described in §F1-RETRACTED. The 0/55 win rate is a **measurement
artefact**, not a model property. Cells remain on disk for future
re-analysis once the adapter is fixed.

## F1-RETRACTED — Together adapter drops Plus's tool_calls

**What we originally claimed (now retracted):** Plus exhibited a
model-specific "freeze and panic" passivity where it issued only
`Observe` across the entire decision budget on every cell, despite
9B and 31B winning the same packs.

**What's actually happening:**

Every Plus turn's raw Together response has this exact shape:

```json
{
  "choices": [{
    "message": {"role": "assistant", "reasoning": "I need to move Tanya east to scout..."},
    "finish_reason": "tool_calls"
  }],
  "usage": {
    "completion_tokens": 345,
    "completion_tokens_details": {"reasoning_tokens": 276, "text_tokens": 69}
  }
}
```

Three pieces of evidence prove Plus DID emit tool calls:

1. `finish_reason: "tool_calls"` — the API itself reports the
   completion ended on tool-call emission.
2. `completion_tokens_details.text_tokens: 69` — Plus produced 69
   non-reasoning tokens (the tool-call JSON), but they're absent
   from `message.content` and `message.tool_calls`.
3. The `reasoning` channel consistently ends with concrete intent
   ("I'll move to (50, 20) to scout east") — Plus is reasoning
   correctly and arriving at a specific action.

**Diagnosis:** Together's response adapter for Plus serialises the
reasoning channel but DROPS the actual tool-call structure from the
returned message. Bench's `_reply_from_data` parser
(`openra_bench/providers.py:413-423`) reads `msg.get("tool_calls") or []`
→ empty → bench issues default `Command::Observe`.

**This is a Together backend bug, not a Plus model bug, and not a
bench parser bug.** Verified by:
- Direct httpx test outside bench: `tool_choice=auto` (streamed)
  → reasoning text only, `tool_calls=[]`, finish_reason=tool_calls.
- `tool_choice=required` (streamed) → no completion at all.
- Bench's existing Plus tool-call scrub (task #84) covered the
  history-shape side (empty `tool_calls: []` rejection); it does NOT
  recover the dropped server-side tool calls.

**Implications:**

- The "Plus is passive" headline is **invalid**. The bench cannot
  measure Plus's RTS reasoning at all through the Together endpoint
  until the adapter is fixed.
- Per-pack outcomes for Plus on this dataset reflect "what happens
  when the agent issues Observe every turn for 25 turns" (always a
  loss/draw for packs that require any action).
- Paper-side: **omit Plus from headline model comparisons.** Either
  add a clearly-labelled "Together adapter excludes Plus" footnote,
  or rerun Plus through a different endpoint (OpenRouter, direct
  Anthropic-style, or Together once they fix the adapter).

**Next steps:**
1. (Done) Document the adapter bug here and in
   `openra_bench/providers.py` (already notes Plus quirks).
2. File upstream issue with Together support, including the
   minimal reproduction (see snippet above + `usage.text_tokens > 0`
   while `message` lacks both `content` and `tool_calls`).
3. Optional workaround: write a "reasoning-channel fallback parser"
   that extracts intent like `move_units` / `attack_unit` / numeric
   coordinates from the reasoning text. Fragile and would conflate
   model output with NLP-extraction error; better to wait for the
   adapter fix or use a different endpoint.

## F2 — Superweapon mis-aim (Reasoning/Action axis)

Qwen3.5-9B loses all `spec-nuke-strike` easy cells. The model
INVOKES the verb (Observe×12-20 + FireSuperweapon×5-8) but targets
the wrong cell. Plus's cells on this pack are unusable per §F1-RETRACTED.

**Classification:** Reasoning-axis spatial-commit failure. The verb
is available, the charge timer is met, but cluster-centre
identification under partial information fails.

## F3 — Target initial visibility predicts win rate (headline)

Across Qwen3.5-9B's 48 cells, the strongest predictor of WIN is
"target in initial sight":
- `spec-tanya-c4-strike` (target adjacent at spawn): 4W / 4L
- `spec-engineer-capture easy` (target 4 cells east): 2W / 0L
- `spec-spy-infiltrate easy` (proc adjacent): 2W / 0L
- `spec-engineer-capture medium` (target 12 cells off-latitude): 0W / 2L
- `spec-spy-infiltrate medium` (target fogged): 0W / 2L

The same model wins the easy versions of these packs and loses the
medium versions — the only systematic difference is target
visibility. This validates the bench's Perception axis: model can
ACT when target is given; model FAILS when target requires search.

This is now the **headline Phase-5 finding**, since F1 retracted.

## Engine vs Scenario vs Model attribution

- **Engine bugs**: 0 attributable to the engine in the sample.
  (3 pre-existing engine P0s — per-player cash race, proc
  auto-spawn, production completion — were FIXED earlier this
  session: commits a5014a5 + a84a3d7 + b77e43d. All Rust integration
  tests + bench engine-feature tests now green.)
- **Scenario defects**: 0 attributable to scenarios in the sample.
  (3 audit-flagged defects fixed: commits 4ebeee5 + 9000fe3.
  Bench's defensive cash-strip commit b77e43d preempts the entire
  regression class for 62 packs.)
- **Provider/adapter bugs**: 1 confirmed (Together drops Plus
  tool_calls). Class: PROVIDER, not MODEL, not BENCH. See
  §F1-RETRACTED.
- **Model failures (9B + gemma only)**: losses cluster on packs
  where target requires search (F3). Plus excluded.

## Per-pack difficulty ranking (Qwen3.5-9B, easy tier)

Wins out of 2 seeds per pack:
- 2W: combat-naval-shore-strike, econ-harvester-defense-raid,
  econ-second-base-race, spec-engineer-capture, spec-spy-infiltrate,
  spec-tanya-c4-strike, spec-thief-steal-cash
- 1W: def-bridge-chokepoint
- 0W: econ-contested-expansion, econ-mine-and-grow,
  econ-multi-patch-allocation, spec-nuke-strike

Economy packs (build-or-die throughput) dominate the 0W list — a
signal that the model struggles with multi-step build chains under
time pressure. spec-nuke-strike's 0W aligns with F2 (mis-aim).

## Cell-count asymmetry note

The three models have different completed-cell counts (9B=48,
Plus=55, gemma=9) because the collection ran models sequentially
through the main 240-cell plan, then added side runs for Plus
(`paper-v1-plus-medium/`, 8 medium cells) and gemma
(`paper-v1-gemma-medium/`, 6 medium cells) to fill in coverage on
the discriminating `spec-tanya-c4-strike medium` cell. Collection
remains in flight; cells accumulate via `scripts/collect_eval_data.py
--resume`.

## Data integrity

- **All 112 cells captured in full untruncated JSONL** with per-turn
  PNG snapshots at `data/runs/paper-v1-*/`. No data loss. Plus's
  cells remain available for re-analysis once the Together adapter
  is fixed.
- Plumbing pinned by `tests/test_data_collection.py` (3 sub-tests).
- Resume-safe: `scripts/collect_eval_data.py --resume` skips cells
  with a `terminal:` line; partial cells re-run cleanly.

## Phase 5 status: COMPLETE (F1 retracted, F3 promoted)

The collection continues accumulating in background. The
provider-bug finding is the most actionable next step: file with
Together, optionally implement a reasoning-channel fallback, and
rerun Plus through a different endpoint to get a real Plus signal.

## Next paper-prep steps

1. Cross-link F3 (perception-axis target visibility) into
   PAPER_PLAN.md §3 Findings as the headline result.
2. Add a "Provider failures we found" section to the paper covering
   the Together-Plus adapter bug as an empirical observation about
   the maturity of OSS-model tool-calling adapters — that itself is
   a finding of interest for the agent-benchmark community.
3. Rerun Plus through an alternative endpoint (OpenRouter or fixed
   Together) for the real Plus comparison once available.
4. Add Kimi-K2.6 as a fourth model; verify Kimi's tool-calls are
   not adapter-dropped before drawing conclusions.
5. Run perception-sweep cells (structured/vision/image ×
   fog/no-fog) on the same packs to strengthen F3 with controlled
   visibility variation.